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Particulate air pollution on cardiovascular mortality in the tropics: impact on the elderly

Background

Air pollution has a significant health impact on mortality and morbidity worldwide, resulting in an estimated 4.2 million deaths and 103.1 million disability–adjusted life-years lost. Air pollution has wide-ranging systemic effects on the human body, impacting both the respiratory, and cardiovascular systems via multiple mechanisms including oxidative stress, inflammation and endothelial dysfunction.

Many prospective cohort and daily time-series studies published globally have consistently demonstrated the negative associations between long and short-term exposure to air pollution and human health [3–9]. These studies did not only establish associations between ambient particulate matters and respiratory health, but also on cardiovascular health, with the elderly being an especially susceptible group [10–12]. However, the vast majority of these studies were conducted in temperate regions [3–9] rather than in the tropics. As seasonal variations and temperature changes have been shown to impact the relationship of air pollution on health outcomes [13–15], we aim to study the impact of air pollution on non-accidental and cardiovascular mortality in the general population, as well as the elderly, in an equatorial country with tropical climate and no seasons.

Data sources

Singapore is an Asian city-state, situated near the equator with a tropical climate, comprising 5.40 million of people. Besides being exposed to daily ambient air pollution generated from domestic sources, Singapore is also exposed almost yearly to haze episodes of about a month long duration, whereby smoke from regional forest fires especially during the dry seasons is blown by winds from neighbouring countries.

Air pollutant and meteorological data are comprehensively collected in Singapore. Daily average of 24-h concentrations for particulate matters smaller than 10 μm and 2.5 μm (PM10 and PM2.5), 8-h carbon monoxide (CO), 24-h nitrogen dioxide (NO2), 8-h ozone (O3) and 24-h sulphur dioxide (SO2) from the years of 2001 to 2013 were obtained from the National Environment Agency (NEA) Singapore. During this 13-year study period, air pollution in Singapore was monitored in air monitoring stations located at various sites around Singapore. Four stations located at road-sides were excluded from the study because they did not reflect the daily exposure to air pollutants amongst the general population. Our final analysis included data from 18 studied stations and data completeness for each studied station was assessed by calculating the proportion of days on which data was collected out of the number of days in operation. Aggregated daily air pollutant concentrations were calculated following the Air Pollution on Health: European Approach (APHEA) protocol with added modifications. In summary, imputed annual mean concentrations for each station were subtracted from the station-specific daily concentrations of the same year to generate a set of ‘centred’ values. Using the centred values, the arithmetic mean was calculated across all stations by day and the average of the imputed annual mean concentrations were then added back to derive the daily values used in the analysis. Average meteorological values were derived from daily means of dry bulb temperature and relative humidity from 5 stations which selected based on the shortest proximity to the air monitoring stations provided by NEA’s Meteorological Service division.

Mortality data from the Registry of Births & Deaths were extracted to calculate aggregated daily counts of all non-accidental deaths (International Classification of Diseases (ICD)-9000–799 from the period of 2001–2012 & ICD-10 A00-R99 for the year of 2013) and cardiovascular deaths (ICD-9390–459 & ICD-10 I00-I99) by age groups (all-age, < 65 and ≥ 65 years) over the 13 years study period. A separate analysis was also conducted looking at the subset of subjects ≥ 80 years. Ethics approval was obtained from the SingHealth Centralised Institutional Review Board.

Statistical analysis

A quasi-Poisson generalized additive model was used for the analysis. Single-day lag models and distributed lag models (DLMs) were respectively built to analyse the lagged day effects of the different air pollutants first on all ages combined non-accidental and cardiovascular mortality [18–20] respectively. Analyses of PM10 and PM2.5 effects stratified by two age groups (< 65 and ≥ 65 years) were then repeated using the DLMs to further study age-specific effects.

Separate core models were firstly built (for each age group and mortality type) without adding pollutant variable to explain the variations, as much as possible, due to long term trends and other potential time-varying confounding factors:\documentclass[12pt]{minimal}

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\begin{document}$$ Log\ E\left[(Y)\right]=\alpha +s(T)+{\beta}_1 DOW+{\beta}_2 SARS+{\beta}_3 FLU+{\beta}_4 PH+{\beta}_5 afterPH+s(DBT)+s(RH) $$\end{document}LogEY=α+sT+β1DOW+β2SARS+β3FLU+β4PH+β5afterPH+sDBT+sRH

Where Log is the natural logarithm, Y is the daily counts of non-accidental or cardiovascular deaths, DOW refers to the day of week and s(T), s(DBT) and s(RH) refer to the penalised cubic regression smoothers for long term trends, dry bulb temperature (°C) and relative humidity (%) respectively. 1 to 3 days lag were tested for variables DBT and RH. Selection of lag-day variables and smoothing spline parameters were based on models giving the lowest quasi-Akaike’s Information Criterion. Dummy variables of SARS and FLU are used to control for impact due to the periods of 2003 severe acute respiratory syndrome (SARS) and of the 2009 influenza A(H1N1) pandemics respectively while PH and afterPH are terms for public holidays and the day following public holidays.

After the core model was established and chosen for each mortality outcome, variables depicting the various air pollutants were added for the single-day lag models. Two-pollutant models were further constructed for pollutants with significant effects in the single pollutant model. For the DLMs, basis functions were applied with the lag-response relationship defined a priori using a 3rd degree polynomial. Polynomials had been used in previous air pollution studies to analyse the lag-response associations [22–24]. A 30-day lag structure, signifying a medium-term period of about a month, was used to observe cumulative effects and mortality displacement if any. As a sensitivity analysis, cumulative lag-response charts generated with DLMs using 2nd and 4th degrees polynomials are shown in the Additional file 1: Figures S1 to S4. Percentage changes in mortality risk, or excess risks (ERs), associated with a 0.1 mg/m3 for CO and 10 μg/m3 for other pollutants were calculated using (RR-1) × 100%, where RR denotes relative risk estimated from the regression coefficient of air pollutant variable of the models. Statistical significance was assessed using 95% confidence intervals (CI). Residual autocorrelation (ACF) and partial autocorrelation (PACF) charts for the core models were performed to assess model fit and are shown in Additional file 1: Figure S5. Data collection process might be interrupted for certain stations over the study period due to site relocation or site closure, and hence annual mean pollutant concentrations used for centring the data were imputed using spatial interpolation with the “fields” package version 8.3–6 in the R statistical software. All statistical analyses, including plotting of lag-response relationships and residual diagnostics to assess model fit, were carried out using the “mgcv” and “dlnm” packages in R version 3.3.1 [17, 19, 26].

Air pollution and mortality

The low residual ACF and PACF values (Additional file 1: Figure S5) showed that the core models were able to explain the temporal trends of the data adequately. Significant effects were observed for PM10 & PM2.5 in both the single-day and distributed lag models. In the single-day lag models, the highest ER associated with a 10 μg/m3 increase occurred in the 3rd day after exposure for both non-accidental (PM10 ER: 0.627, 95% CI: 0.260–0.995% and PM2.5 ER: 0.660%; 95% CI: 0.204–1.118%) and cardiovascular mortality (PM10 ER: 0.897%; 95% CI: 0.283–1.516% and PM2.5 ER: 0.883%; 95% CI: 0.121–1.621%). The estimated ERs generally remained significant for PM10 and PM2.5 even after controlling for additional pollutants (Additional file 1: Table S2a,b).

O3 also exhibited a significant effect at day 1 lag for non-accidental mortality (ER: 0.354%; 95% CI: 0.011–0.698%). However, this effect was insignificant after adjusting for PM10 and PM2.5 (Additional file 1: Table S2c). Effects by other pollutants were observed to be minimal or insignificant (Table 2).

Results from the distributed lag models are presented in Table 3 and Fig. 1. Significant cumulative effects were observed over days 0–5 but not over subsequent longer periods. Cumulative ERs over days 0–5 for PM10 & PM2.5 were estimated to be 0.700% (95% CI: 0.276–1.126%) & 0.829% (0.276–1.386%) respectively for non-accidental mortality, and 0.921% (0.218–1.629%) & 1.073% (0.157–1.998%) respectively for cardiovascular mortality. Sensitivity analysis using 2nd or 4th degree polynomials showed similar results (Additional file 1: Figures S1-S4).

Impact by age

Cumulative effects were significant in the elderly aged 65 years old and above, with every 10 μg/m3 increase in pollutant concentration resulting in a 0.771% (95% CI: 0.265–1.279%) and a 0.955% (0.297–1.618%) increase over days 0–5 in non-accidental mortality risk for PM10 & PM2.5 respectively. Cardiovascular mortality in turn showed a 1.236% (0.436–2.042%) and a 1.478% (0.437–2.530%) increase in risk of PM10 & PM2.5 over days 0–5 (Table 4).

No significant effects were seen over days 0–15, but interestingly significant negative cumulative effects were seen for cardiovascular mortality over days 0–30 in the elderly group (PM10 ER: -2.085%; 95% CI: -3.614% – -0.532% and PM2.5 ER: -2.721%; 95% CI: -4.667% – -0.736%). A similar negative effect was also seen for the younger age group for non-accidental mortality over days 0–30 for PM2.5 (ER:-1.694%; 95% CI: -3.220% – -0.143%). Sensitivity analysis using up to 2nd or 4th degree of polynomials showed similar results (Additional file 1: Figures S1-S4).

In a separate analysis looking at the very elderly aged 80 years and above, cumulative effects were significant, with every 10 μg/m3 increase in pollutant concentration resulting in a 0.749% (95% CI: 0.095–1.408%) and a 0.955% (0.105–1.812%) increase over days 0–5 in non-accidental mortality risk for PM10 & PM2.5 respectively (data not shown). There was a trend towards increased cardiovascular mortality at days 0–5, although this association was not significant. Significant negative cumulative effects were seen for cardiovascular mortality over days 0–30 in this very elderly group for PM10 (ER: -2.060%; 95% CI: -3.987% – -0.094%) and a similar non-signficant trend for PM2.5.

Lag-response association

Graphs showing non-cumulative percentage change in mortality over days 0–30 are shown in the Additional file 1: Figure S6, S7. The graphs for the all-age group and the ≥ 65 years age group showed that impact on mortality was mostly observed in the immediate days (days 0–5) after exposure. After this initial period, significant protective effects were actually observed in both non-accidental & cardiovascular mortality. However, while non-accidental mortality showed effects approaching the null around days 15–20, cardiovascular mortality only showed a rebound around days 20–25, thus indicating a slower approach.

Limitations

There are several limitations. Firstly, due to the time-series analysis nature of the study, unmeasured clinical variables (eg. comorbidities), which could affect the actual estimates of air pollution on mortality, could not be fully accounted for despite our best efforts in controlling for potential confounding factors in the models. Our models also did not adjust for the transboundary haze episodes as we wanted to analyse the general exposure of the public to air pollutants, and not to distinguish between domestic and transboundary air pollution. Secondly, the impact of indoor air pollution was not assessed as this data was not readily available, but we believe such effect would be minimal as residents in Singapore do not burn coals for cooking in the houses. Thirdly, a 3rd degree of polynomials was used to define the lag-response relationship over a period of 30 days. A different parametric shape or a different degree parameter would result in different ER estimates. However, a sensitivity analysis using up to 2nd or 4th degree of polynomials showed that these differences were minute and did not affect our final interpretation. A fourth limitation is that we did not explore the inclusion of different concentration-response functions to describe the exposure-response relationship. In our analysis, we assumed a log-linear relationship via a quasi-Poisson model. Lastly, correlation between the paired pollutants in the two-pollutant models potentially resulted in multicollinearity that produced some unstable estimates.

Conclusions

These first contemporary population-based data from an equatorial country with tropical climate and no seasons show that exposure to particulate air pollution (PM2.5, PM10) was significantly associated with non-accidental mortality and cardiovascular mortality, especially in the elderly.